Beyond Implementation: Neuroanesthesiologists as Artificial Intelligence CoCreators.

Beyond Implementation: Neuroanesthesiologists as Artificial Intelligence CoCreators - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice editorial J Neurosurg Anesthesiol . 2026 Apr 14;38(3):191–193. doi: 10.1097/ANA.0000000000001113 Beyond Implementation: Neuroanesthesiologists as Artificial Intelligence CoCreators Meredith C B Adams 1, ✉ Author information Article notes Copyright and License information 1 Department of Anesthesiology, Artificial Intelligence, Translational Neuroscience, and Public Health Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC ✉ Corresponding author. Received 2025 Oct 7; Accepted 2025 Dec 2; Issue date 2026 Jul. Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13241182  PMID: 41979208 Artificial intelligence (AI) is rapidly transforming health care, yet neuroanesthesiology stands at a unique crossroads. While numerous FDA-approved AI/ML tools are already in clinical use, current models underperform in several spheres. 1 This disconnect reveals a critical truth: the most sophisticated analytics are meaningless without clinical context. As health care data grows exponentially and demand for precision care expands, neuroanesthesiologists must move beyond passive adoption to an active partnership in developing AI systems that genuinely enhance patient care. The question is not whether AI will transform our field, but whether we will shape that transformation. True clinical relevance in neuroanesthesia requires reconciling 3 often divergent perspectives: patient-centered outcomes, clinical workflow efficiencies, and health system value. These perspectives exist in current clinical care environments, but developing algorithms that formalize them requires clarity and alignment. The challenge and opportunity lie in developing AI systems that satisfy these perspectives. This will require active participation in defining success metrics that extend beyond traditional anesthetic endpoints to encompass functional neurological outcomes not typically captured in perioperative datasets. The discussion surrounding AI adoption in health care has become focused on the “black box” problem, the concern that complex algorithms obscure their decision-making processes from human understanding. Yet this framing misses a more fundamental question: who possesses the expertise to meaningfully interrogate these systems in the first place? The challenge is not merely achieving algorithmic transparency, but ensuring that those developing, validating, and implementing AI in clinical settings possess the domain knowledge necessary to critically evaluate its outputs. 2 As neuroanesthesiologists confront the proliferation of AI tools promising to revolutionize perioperative neuroscience, we must recognize that successful adoption requires deep clinical expertise guiding technical implementation, not the reverse. 3 Evidence across medical specialties demonstrates a clear pattern: clinician-led AI development consistently outperforms technology-first approaches. When domain experts actively participate, the resulting systems better capture clinical nuance and achieve meaningful improvements in patient care. 4 The current landscape reveals an expertise gap, with algorithms sometimes trained on inadequate datasets, validated against inappropriate benchmarks, or deployed without sufficient consideration for clinical workflow integration. 5 Recent advances in federated learning offer promise by enabling collaborative development that keeps patient data at originating institutions while allowing model refinement across diverse environments, a framework that naturally emphasizes clinician involvement throughout the development lifecycle. 6 Clinical expertise becomes particularly critical in perioperative neuroscience, where the complexity of brain-anesthesia interactions defies simple algorithmic solutions. The effects of anesthetic agents on consciousness, cerebral metabolism, and functional connectivity involve nonlinear dynamics that even experienced practitioners find challenging. 7 The multimodal data streams integral to neuroanesthesia care, electroencephalography (EEG), intracranial pressure monitoring, cerebral oximetry, transcranial doppler, and evoked potentials, each require specialized interpretation that depends heavily on clinical context. 5 An algorithm may detect patterns in EEG data, but clinicians understand how those patterns change meaning in the context of surgical manipulation, anesthetic depth, patient temperature, and underlying pathology. The patient safety implications warrant careful attention. Generic AI models, even when technically sophisticated, may struggle in neurocritical scenarios where subtle changes in monitored parameters could herald impending complications or simply reflect benign physiological variation. Neuroanesthesiologists distinguish between these possibilities by integrating information across multiple domains simultaneously, a reasoning process that current AI systems cannot replicate without explicit guidance from experts who understand the clinical significance of these integrations. Validation frameworks must prioritize clinical relevance over purely technical metrics, demanding demonstration of utility in real clinical environments and evaluation against outcomes that matter to patients. 8 Examining successful AI implementations across medical specialties reveals consistent themes: domain expertise guides not only initial development but also ongoing validation and implementation. The most impactful clinical AI systems emerge from iterative collaboration between clinicians who understand problems deeply and technologists who can translate that understanding into computational solutions. 4 This collaborative model requires mutual learning, clinicians developing AI literacy to engage meaningfully with technical decisions, while technologists acquire sufficient clinical understanding to appreciate subtleties that determine whether algorithms truly serve patient care. 9 The path forward for neuroanesthesia AI requires positioning ourselves as co-developers, not merely end-users (Fig. 1 ). Realizing potential requires active engagement from our specialty throughout the development process. 1 We should establish training pathways that equip neuroanesthesiologists with quantitative skills and computational literacy necessary to contribute substantively to AI projects, while encouraging developers working in our domain to invest time understanding perioperative neuroscience at levels enabling productive collaboration. Similar to other clinical domains, the roadmap for successfully integrating AI into clinical practice requires clear articulation of clinical problems, alignment of AI methodologies with those problems, and rigorous evaluation of clinical impact. 10 Without clinical impact, the most novel algorithms fall short. FIGURE 1. Open in a new tab Collaborative co-development model for AI in neuroanesthesia: Centering Clinical Relevance and Technical Innovation. This schematic illustrates the collaborative co-development framework for artificial intelligence tools in neuroanesthesiology, emphasizing the cyclical and iterative nature of effective AI development. The model depicts a continuous cycle of 5 interconnected stages arranged clockwise: (1) Clinician-Developer Collaboration, representing the equal partnership between domain experts and technologists; (2) Problem Definition, where clinical questions and needs are identified; (3) Feature Engineering, involving selection and optimization of relevant data streams and variables; (4) Validation Framework development, establishing appropriate metrics and benchmarks for clinical relevance; and (5) Implementation and Refinement, addressing real-world deployment and ongoing optimization. Neuroanesthesiologists will need essential competencies to be able to recognize the difference between AI models, not to build them, but to understand their respective strengths and failure modes. Models that perform brilliantly on training data may generalize poorly to your institution’s patient population. A model developed exclusively on academic medical center data may fail in community hospitals with different case mixes and resource constraints. Does high overall accuracy mask poor performance in specific subgroups, perhaps elderly patients or those with rare conditions underrepresented in training data? Understanding metrics such as the area under the receiver operating characteristic curve (AUC), positive predictive value, and calibration enables a critical evaluation of whether a promising algorithm is ready for clinical deployment. Anesthesiologists possess unique expertise in physiology, clinical uncertainty, and the consequences of perioperative decisions. This expertise should inform AI development from inception through implementation. Our specialty benefits most when we lead rather than follow AI development in our domain. True partnership with technology developers requires our active participation in building these systems. 2 , 3 We have the clinical knowledge necessary to guide AI toward genuinely useful applications while helping avoid potential pitfalls. The black box problem will be addressed not merely by making algorithms more transparent, but by ensuring that experts with the knowledge to interpret them are meaningfully involved in their creation. The question before us is whether we will exercise that expertise proactively to shape the future of perioperative neuroscience care. Footnotes Research reported in this publication was supported by NIH HEAL Initiative, National Institute on Drug Abuse, NIH Office of the Director, and National Institute of Neurological Disorders and Stroke under grant numbers R24DA055306, R24DA055306-01S1, R24DA055306-02S1, U24DA057612, U24DA058606, and R25DA061740. This conceptual work was presented as the SNACC 2025 Annual Meeting Keynote in Atlanta, Georgia, on 09-13-2025. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no conflicts of interest to declare. REFERENCES 1. Nozari A, Binda DD, Baker MB, et al. Advancements in neuroanesthesia through artificial intelligence. Anesthesiol Clin. 2025;43:527–547. doi: 10.1016/j.anclin.2025.05.006 [ DOI ] [ PubMed ] [ Google Scholar ] 2. Pierce RL, Van Biesen W, Van Cauwenberge D, et al. Explainability in medicine in an era of AI-based clinical decision support systems. Front Genet. 2022;13:903600. doi: 10.3389/fgene.2022.903600 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Hildt E. What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach. Bioengineering (Basel). 2025;12:375. doi: 10.3390/bioengineering12040375 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Hirosawa T, Suzuki T, Shiraishi T, et al. Adapting Artificial Intelligence Concepts to Enhance Clinical Decision-Making: A Hybrid Intelligence Framework. Int J Gen Med. 2024;17:5417–5422. doi: 10.2147/ijgm.S497753 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Banerji CRS, Bhardwaj Shah A, Dabson B, et al. Clinicians must participate in the development of multimodal AI. EClinicalMedicine. 2025;84:103252. doi: 10.1016/j.eclinm.2025.103252 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Hagestedt I, Hales I, Boernert E, et al. Toward a tipping point in federated learning in healthcare and life sciences. Patterns (N Y). 2024;5:101077. doi: 10.1016/j.patter.2024.101077 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Varley TF, Luppi AI, Pappas I, et al. Consciousness and brain functional complexity in propofol anesthesia. Sci Rep. 2020;10:1018. doi: 10.1038/s41598-020-57695-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Wells BJ, Nguyen HM, McWilliams A, et al. A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare. NPJ Digit Med. 2025;8:514. doi: 10.1038/s41746-025-01900-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Quinn TP, Senadeera M, Jacobs S, et al. Trust and medical AI: the challenges we face and the expertise needed to overcome them. J Am Med Inform Assoc. 2021;28:890–894. doi: 10.1093/jamia/ocaa268 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Adams MCB, Bowness JS, Nelson AM, et al. A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements. Curr Opin Anaesthesiol. 2025;38:680–688. doi: 10.1097/aco.0000000000001508 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Articles from Journal of Neurosurgical Anesthesiology are provided here courtesy of Wolters Kluwer Health ACTIONS View on publisher site PDF (148.7 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Trustworthy Artificial Intelligence in Dentistry: Learnings from the EU AI Act#163474
• LEGAL ASPECTS OF THE USE OF ARTIFICIAL INTELLIGENCE IN TELEMEDICINE-BASED DIAGNOSIS: BETWEEN AID TOOLS MEDICAL AND CLINICAL DECISION MAKING#423715
• Improving the Accuracy of Artificial Intelligence Triage in Primary Care#869041
• AI in medical devices: regulatory challenges and the path forward#161427
• AI-driven Clinical Decision Support for Perioperative Blood Orders#834836